Skip to main content

Google Ai

 Step 1: Access Google Studio

- Go to Google Studio

- ⁠Click on stream

- ⁠Select Share Screen

Link: https://aistudio.google.com/

Step 2: Share your Screen

- Click on Share Screen 

- ⁠Select Chrome tab, Window or entire screen 

- ⁠It is like letting an Ai join an online meet session.

Step 3: Start asking questions

- Ask any question about what it sees on your screen and it will direct you step-by-step. 

There has never been a better to learn anything 🙀.

Pro tip: It’s Free

Doesn’t work on Phones





Comments

Popular posts from this blog

Python Road map

 

Command on Run

 🔰 23 Important Commands in the RUN (Executer) List 🔹 The command dxdiag: Used to check all the specifications of your device.   🔹 The command cleanmgr: Opens the Disk Cleanup tool.   🔹 The command temp: Accesses temporary files, which we delete as they contribute to slowing down the computer.   🔹 The command regedit: Opens the Registry Editor.   🔹 The command calc: Opens the Calculator.   🔹 The command msconfig: A tool to access programs that run with Windows at startup and disable them to speed up the system.   🔹 The command scandisk: Used for disk checking.   🔹 The command cmd: Opens the Command Prompt for Windows.   🔹 The command defrag: Used to stop and defragment the hard drive.   🔹 The command taskman: Allows you to see what is open in the taskbar and manage it.   🔹 The command pbrush: Opens the Paint program in Windows.   🔹 The command debug: Used to ch...

Is it the end of lora?

 Is this the end of LoRA as a fine-tuning approach? Singular Value fine-tuning is here! We have a new paper in town called the "Transformers Squared". It promises to adapt any LLM to any task without external intervention.  The core idea of the paper is to use Singular Value Decomposition (SVD) to factorize the weight matrices of transformers. During training, we learn different singular values for different tasks. More specifically, we learn to scale the singular values for different tasks. Tasks can be as diverse as math reasoning or coding. The possibilities are endless. During inference, we do a two-pass inference. In the first pass, the LLM decides which "scale" to use for which task. In the second pass, the LLM adapts itself to be a specialist in the task and responds to our prompt. How cool is that? Paper Title: Transformer2: Self-adaptive LLMs Paper: https://sakana.ai/transformer-squared Blog: https://arxiv.org/abs/2501.06252 Video Explanation: https://youtu...